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The prompt templates used within the Self-Instruct framework for generating new instructions and fine-tuning samples are not necessarily fixed. While simple templates can serve as a starting point, they can be refined and made more sophisticated to yield instructions and samples that are more diverse and accurate, thereby improving the quality of the final fine-tuning dataset.

Refining Prompt Templates in Self-Instruct

Based on the process described in the case study, what is the most probable weakness in the generation process causing the lack of diversity, and what specific change would best address this issue?

Diagnosing Low Diversity in a Generated Dataset

A machine learning team is using a large language model to iteratively generate a large dataset of instructions and corresponding input-output pairs, starting from a small seed set. They employ a single, simple, and unchanging prompt structure to request new data from the model throughout the entire generation process. Analyze the potential negative consequences of this approach on both the quality of the final dataset and the capabilities of a new model that is subsequently fine-tuned on this data.

Consequences of Static Prompt Structures in Automated Data Generation

When using Large Language Models to synthetically generate data for specific tasks, such as text classification, a potential issue is the emergence of biased predictions. This can manifest as an imbalance in the generated samples, where the majority of instances fall into a single category, leading to a skewed dataset.

Biased Predictions in LLM-based Synthetic Data Generation

An AI development team is using a large language model to automatically generate a dataset of programming problems and their solutions. They start with a simple instruction-generation prompt like: `Generate a new programming problem.` After generating 10,000 examples, they find that the problems are repetitive (e.g., mostly sorting lists) and the generated solutions are often suboptimal. Which of the following modifications to their process would be the most effective first step to improve both 

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